A risk-unbiased bound for information fusion with nuisance parameters

Shahar Bar, Joseph Tabrikian

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Multi-sensor information fusion usually involves existence of nuisance parameters, such as the estimation error covariance at each node of the network. In this paper, we address the question of how accurately one can estimate a parameter of interest using a network of multi-sensors, subject to unknown noise intensity at the sensors. The commonly used Cramér-Rao bound (CRB) is restricted to mean-unbiased estimation of all model parameters with no distinction of their character and leads to optimistic and unachievable performance analysis. Instead, a Cramér-Rao-type bound on the mean-squared-error (MSE) is derived for the considered scenario, where the noise variances are considered as nuisance parameters. The proposed bound is based on the risk-unbiased CRB (RUCRB), which assumes risk-unbiased estimation of the parameters of interest. Simulations show that the RUCRB provides a tight and achievable performance analysis for the MSE of conventional estimators.

Original languageEnglish
Title of host publicationFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages504-511
Number of pages8
ISBN (Electronic)9780996452748
StatePublished - 1 Aug 2016
Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany
Duration: 5 Jul 20168 Jul 2016

Publication series

NameFUSION 2016 - 19th International Conference on Information Fusion, Proceedings

Conference

Conference19th International Conference on Information Fusion, FUSION 2016
Country/TerritoryGermany
CityHeidelberg
Period5/07/168/07/16

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